In general, statistical methods have two categories: parametric and nonparametric. Parametric analysis is usually made based on information regarding the probability distribution of the random variable. While, nonparametric method is also referred as a distribution-free procedure, which does not require prior knowledge of the distribution of the random variable. In reality, few cases allow practitioners to gain full knowledge of a random variable and tell the probability distribution for sure. Hence, there are two choices for practitioners. One can still use the parametric methods due to the scientific evaluations or the simplification of situation, with an assumption of the parametric distribution. Alternatively, one can directly apply the nonparametric methods without having much knowledge of the distribution. The conclusions from the parametric methods are valid as long as the assumptions are substantiated. These assumptions would help solving problems, but also risky because making a wrong assumption might be dangerous. Hence, nonparametric techniques would be a preferable alternative. One chief advantage of the nonparametric methods lies in its relaxation of the shapes of the distributions, namely, distribution-free property. Hence, from a research point of view, new methodology with nonparametric techniques applied, or further investigation related to existing nonparametric techniques could be interesting, informative and valuable. All research in this matter contributes to univariate nonparametric tests and control charts.